Namespaces

Namespaces worden gebruikt om vectoren binnen één index te scheiden, zodat je gericht kunt query'en en het aantal gescande records minimaal blijft.

In deze oefening ga je vectoren inladen in verschillende namespaces binnen de Pinecone-index 'datacamp-index'. Je moet verbinding maken met de index, vectoren upserten in verschillende namespaces en de bijgewerkte metriek van de index controleren.

Deze oefening maakt deel uit van de cursus

Vector-databases voor embeddings met Pinecone

Cursus bekijken

Oefeninstructies

Initialiseer de Pinecone-verbinding met je API-sleutel.
Upsert de vectoren in vector_set1 in de namespace namespace1.
Upsert de vectoren in vector_set2 in de namespace namespace2.
Print de beschrijvende statistieken van de index.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Initialize the Pinecone client with your API key
pc = Pinecone(api_key="____")
index = pc.Index('datacamp-index')

# Upsert vector_set1 to namespace1
____(
  vectors=vector_set1,
  ____
)

# Upsert vector_set2 to namespace2
____(
  vectors=vector_set2,
  ____
)

# Print the index statistics
____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Vector-databases voor embeddings met Pinecone

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Explore the mechanics behind Pinecone's vector database, from pods and indexes to comparing it with other databases. Learn to differentiate pod types, acquire API keys, and initialise Pinecone connection using python. Finally, you’ll learn how to create Pinecone indexes, exploring different parameters such as dimensionality, distance metrics, pod types, and others.

Exercise 1: Introduction to Pinecone indexes Exercise 2: Creating a Pinecone client Exercise 3: Your first Pinecone index Exercise 4: Managing indexes Exercise 5: Connecting to an index Exercise 6: Deleting an index Exercise 7: The Pinecone ecosystem Exercise 8: Vector ingestion Exercise 9: Checking dimensionality Exercise 10: Ingesting vectors with metadata

Get hands-on with Pinecone in Python, where we explore the practical side of using Pinecone for managing indexes, adding vectors with metadata, searching and retrieving vectors, and making updates or deletions. Gain a solid grasp of the key functions and ideas to smoothly handle data in the Pinecone vector database.

Exercise 1: Retrieving vectors Exercise 2: Querying vs. fetching Exercise 3: Fetching vectors Exercise 4: Querying vectors Exercise 5: Returning the most similar vectors Exercise 6: Changing distance metrics Exercise 7: Metadata filtering Exercise 8: Filtering queries Exercise 9: Multiple metadata filters Exercise 10: Updating and deleting vectors Exercise 11: Updating vector values Exercise 12: Updating vector metadata Exercise 13: Deleting vectors

In this chapter, learners delve into optimizing Pinecone index performance, leveraging multi-tenant namespaces for cost reduction, building semantic search engines, and creating retrieval-augmented question answering systems using Pinecone with the OpenAI API. Through these lessons, learners gain practical skills in performance tuning, semantic search, and retrieval-augmented question answering, empowering them to apply Pinecone effectively in real-world AI applications.

Exercise 1: Upserts batchgewijs uitvoeren Exercise 2: Een functie definiëren voor chunking Exercise 3: Upserts batchen in chunks Exercise 4: Upserts batchen en parallel uitvoeren Exercise 5: Multitenancy en namespaces Exercise 6: Namespaces

Huidige oefening

Exercise 7: Query's uitvoeren op namespaces Exercise 8: Semantisch zoeken met Pinecone Exercise 9: Een Pinecone-index maken en configureren Exercise 10: Vectors upserten voor semantisch zoeken Exercise 11: Vectoren opvragen voor semantisch zoeken Exercise 12: RAG-chatbot met Pinecone en OpenAI Exercise 13: YouTube-transcripten upserten Exercise 14: Een retrieval-functie bouwen Exercise 15: RAG-vraagantwoordfunctie Exercise 16: Gefeliciteerd!